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Rules and Variables in Neural Nets
Venkat Ajjanagadde1, Lokendra Shastri1
1Computer and Information Science Department, University of Pennsylvania, Philadelphia, PA 19104 USA.
Neural Computation
|May 31, 2019
Summary
Connectionism faces the challenge of representing dynamic structures, known as the variable binding problem. This study presents a biologically plausible solution using rhythmic activity patterns for efficient knowledge representation and predictive reasoning.
Area of Science:
- Cognitive Science
- Computational Neuroscience
- Artificial Intelligence
Background:
- Connectionism struggles with creating and representing dynamic structures.
- The variable binding problem is central to reasoning with systematic and abstract knowledge.
Purpose of the Study:
- To propose a biologically plausible solution to the variable binding problem.
- To outline a knowledge representation and reasoning system utilizing this solution for efficient predictive inferences.
Main Methods:
- Developing a system that propagates rhythmic patterns of activity.
- Representing dynamic bindings through the synchronous firing of nodes.
Main Results:
- A novel approach to solving the variable binding problem in connectionist systems.
- Demonstration of extreme efficiency in a class of predictive inferences.
Conclusions:
- The proposed rhythmic activity pattern method offers a viable solution for dynamic structure representation.
- This approach enhances the efficiency of knowledge representation and reasoning systems.
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